基于YARN规范的智能电网大数据异常检测

A YARN-based Smart Grid Big Data Abnormal Detection

文章描述了Hadoop早期版本在处理智能电网大数据上的不足,同时分析了YARN规范对Map-Reduce进行改进后对电网大数据处理的优点。文章详细讨论了YARNDPP平台中智能电网大数据处理的编码与实现及YARN-DPP的优势,并以IEEE 118节点的电网作为智能电网大数据异常检测的案例程序,对YARN-DPP平台的硬件环境与软件运行环境进行配置。实验结果表明,针对海量的智能电网状态安全大数据异常检测的程序,YARN-DPP平台具有较好的吞吐量与加速比,可以满足现代智能电网大数据异常检测的需求,在计算速度上比单机串行计算及Map-Reduce计算要快。

The defects of processing smart grid big data in Map-Reduce earty version were also discussed, and the advantages of processing smart grid big data in YARN were also described in this paper. The coding model and implementation and advantages of YAJRN-DPP were also analyzed In order to demonstrate the effectiveness of YARN-DPP, the hardware configuration environments and software running environments had been completed. A serial of simulation experiments in IEEE 118 node grid system were also done. The results and performance analysis demonstrated that good throughput and speedup had been obtained in YAJRN-DPP. It can meet the fast demands in large scale grid system big data processing. The computing speed was faster than sequence computation and Map-Reduce computation.